{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Quantitative Finance\n",
    "\n",
    "Copyright (c) 2019 Python Charmers Pty Ltd, Australia, <https://pythoncharmers.com>. All rights reserved.\n",
    "\n",
    "<img src=\"img/python_charmers_logo.png\" width=\"300\" alt=\"Python Charmers Logo\">\n",
    "\n",
    "Published under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. See `LICENSE.md` for details.\n",
    "\n",
    "Sponsored by Tibra Global Services, <https://tibra.com>\n",
    "\n",
    "<img src=\"img/tibra_logo.png\" width=\"300\" alt=\"Tibra Logo\">\n",
    "\n",
    "\n",
    "## Module 1.6: Moving average\n",
    "\n",
    "<div class=\"alert alert-danger\">\n",
    "    Dev note - I used the wrong definition for \"moving average\" when I created this. This notebook still has value, not sure where to put it though\n",
    "   \n",
    "</div>\n",
    "\n",
    "\n",
    "### 1.6.1: Time Series moving average\n",
    "\n",
    "\n",
    "For time series data, the expected value (mean) of the data has little predictive power. In short, you compute the mean over the timeline you compute for, but this doesn't account for any trend.\n",
    "\n",
    "Further, the mean here is heavily biased depending on the timeframe you collect your data for. An increasing linear trend will have a lower mean for earlier times, and a higher mean for later times.\n",
    "\n",
    "Moving averages account for these problems. They are easier to compute than OLS, or more complicated, models, but capture local trends in the data very well, while also incorporating larger global trends to a lighter degree (depending on your window size).\n",
    "\n",
    "With a Simple Moving Average (SMA), you decide up front on a rolling window period. For instance, you may choose a 20 day rolling window. Then, for each time period $n$, you compute the SMA as:\n",
    "\n",
    "$SMA_n = \\frac{1}{k}\\sum_{n-k}^{n}{X_k}$\n",
    "\n",
    "Practically, this value doesn't exist when $k < n$, so those rows are generally not computed. Some people choose to compute $SMA_n$ with just the \"up to $k$ rows available\". A problem with this approach is that the $SMA$ value is very noisy for the first few values before \"settling down\".\n",
    "\n",
    "As a helpful tip, when plotting or performing an OLS on moving averages, ensure you use the centrepoint of the period in your analysis or visualisation. That is, if you computed $SMA_{10}$ with a window of 5, then you plot that point at 8 (the centrepoint of the window). Note that $SMA_{10}$ uses values between 6 and 10 to compute the value.\n",
    "\n",
    "A problem with SMA is that they can be very slow to incorporate sudden changes in the data. For instance, if you \n",
    "Exponential inputs new data faster, but may produce false positives. \n",
    "\n",
    "To address this problem, the Exponential Moving Average (EMA) more heavily weights new data than old data. This incorporates information faster:\n",
    "\n",
    "${EMA}_n = \\alpha{X_n} + (1-\\alpha){EMA}_{n-1}$\n",
    "\n",
    "Where $\\alpha$ is a smoothing constant, sometimes given as $\\frac{\\beta}{1+k}$, where $\\beta$ is just a different smoothing constant (usually 1 or 2) and $k$ is the period of the moving average (or in alternate forms, the lag). Given both are constants, we can replace with the simple $\\alpha$.\n",
    "\n",
    "The risk with EMA is that new data is incorporated often very heavily, leading to false signals for unusual events and sudden changes. Most heavy analysis will consider by the EMA and SMA, comparing them and coming up with a combined measure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run setup.ipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question\n",
    "\n",
    "1. Do moving averages lead or lag the current price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution / Answer: lag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "1. Compute the Simple Moving Average of a stock price for a $k$ value of 20. Investigate the pandas `rolling` function.\n",
    "2. Compute the Exponential moving average using pandas. There is a `ewm` function available on a series.\n",
    "3. Plot the two methods and examine how they change, and how quickly, with sudden swings in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solutions\n",
    "\n",
    "import pandas as pd\n",
    "import quandl\n",
    "import my_secrets\n",
    "quandl.ApiConfig.api_key = my_secrets.QUANDL_API_KEY\n",
    "\n",
    "prices = quandl.get(\"PERTH/SPASX200A_M\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices['SMA'] = prices.rolling(20).mean().shift(-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Index OEM', 'SMA'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices['EMA'] = prices['Index OEM'].ewm(span=20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extended Exercise\n",
    "\n",
    "A simple trading strategy is to compare the actual the moving average values, and take a position when they cross over. For instance, if the price crosses over from above, go short. If the price crosses over from below, go long.\n",
    "\n",
    "Grab the stock prices for end of day from Quandl for a given stock (the solution uses Disney).\n",
    "\n",
    "Find these crossover points, and compute the profit if you take these positions.\n",
    "\n",
    "Hints:\n",
    "\n",
    "* Create a column that indicates if the closing price is above or below the SMA\n",
    "* What would you get if you ran the `diff` function on this data?\n",
    "* For visualisation, checkout `plt.vlines` for drawing vertical lines (i.e. where the crossover exists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotFoundError",
     "evalue": "(Status 404) (Quandl Error QECx02) You have submitted an incorrect Dataset code. Please check your Dataset codes and try again.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[13], line 6\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmy_secrets\u001b[39;00m\n\u001b[0;32m      4\u001b[0m quandl\u001b[38;5;241m.\u001b[39mApiConfig\u001b[38;5;241m.\u001b[39mapi_key \u001b[38;5;241m=\u001b[39m my_secrets\u001b[38;5;241m.\u001b[39mQUANDL_API_KEY\n\u001b[1;32m----> 6\u001b[0m stocks \u001b[38;5;241m=\u001b[39m \u001b[43mquandl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEOD/DIS\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\quandl\\get.py:48\u001b[0m, in \u001b[0;36mget\u001b[1;34m(dataset, **kwargs)\u001b[0m\n\u001b[0;32m     46\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m dataset_args[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumn_index\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     47\u001b[0m         kwargs\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumn_index\u001b[39m\u001b[38;5;124m'\u001b[39m: dataset_args[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumn_index\u001b[39m\u001b[38;5;124m'\u001b[39m]})\n\u001b[1;32m---> 48\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset_args\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcode\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhandle_column_not_found\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m     49\u001b[0m \u001b[38;5;66;03m# Array\u001b[39;00m\n\u001b[0;32m     50\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dataset, \u001b[38;5;28mlist\u001b[39m):\n",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\quandl\\model\\dataset.py:47\u001b[0m, in \u001b[0;36mDataset.data\u001b[1;34m(self, **options)\u001b[0m\n\u001b[0;32m     45\u001b[0m updated_options \u001b[38;5;241m=\u001b[39m Util\u001b[38;5;241m.\u001b[39mmerge_options(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparams\u001b[39m\u001b[38;5;124m'\u001b[39m, params, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[0;32m     46\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 47\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mData\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mall\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mupdated_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m NotFoundError:\n\u001b[0;32m     49\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m handle_not_found_error:\n",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\quandl\\operations\\list.py:15\u001b[0m, in \u001b[0;36mListOperation.all\u001b[1;34m(cls, **options)\u001b[0m\n\u001b[0;32m     13\u001b[0m     options[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparams\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m     14\u001b[0m path \u001b[38;5;241m=\u001b[39m Util\u001b[38;5;241m.\u001b[39mconstructed_path(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mlist_path(), options[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparams\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m---> 15\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mConnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mget\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     16\u001b[0m response_data \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mjson()\n\u001b[0;32m     17\u001b[0m Util\u001b[38;5;241m.\u001b[39mconvert_to_dates(response_data)\n",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\quandl\\connection.py:38\u001b[0m, in \u001b[0;36mConnection.request\u001b[1;34m(cls, http_verb, url, **options)\u001b[0m\n\u001b[0;32m     34\u001b[0m options[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mheaders\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m headers\n\u001b[0;32m     36\u001b[0m abs_url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (ApiConfig\u001b[38;5;241m.\u001b[39mapi_base, url)\n\u001b[1;32m---> 38\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhttp_verb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mabs_url\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\quandl\\connection.py:50\u001b[0m, in \u001b[0;36mConnection.execute_request\u001b[1;34m(cls, http_verb, url, **options)\u001b[0m\n\u001b[0;32m     45\u001b[0m response \u001b[38;5;241m=\u001b[39m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mhttp_verb,\n\u001b[0;32m     46\u001b[0m                            url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m     47\u001b[0m                            verify\u001b[38;5;241m=\u001b[39mApiConfig\u001b[38;5;241m.\u001b[39mverify_ssl,\n\u001b[0;32m     48\u001b[0m                            \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[0;32m     49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m200\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m300\u001b[39m:\n\u001b[1;32m---> 50\u001b[0m     \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_api_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\quandl\\connection.py:114\u001b[0m, in \u001b[0;36mConnection.handle_api_error\u001b[1;34m(cls, resp)\u001b[0m\n\u001b[0;32m    103\u001b[0m d_klass \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    104\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m'\u001b[39m: LimitExceededError,\n\u001b[0;32m    105\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m'\u001b[39m: InternalServerError,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    110\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m'\u001b[39m: ServiceUnavailableError\n\u001b[0;32m    111\u001b[0m }\n\u001b[0;32m    112\u001b[0m klass \u001b[38;5;241m=\u001b[39m d_klass\u001b[38;5;241m.\u001b[39mget(code_letter, QuandlError)\n\u001b[1;32m--> 114\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m klass(message, resp\u001b[38;5;241m.\u001b[39mstatus_code, resp\u001b[38;5;241m.\u001b[39mtext, resp\u001b[38;5;241m.\u001b[39mheaders, code)\n",
      "\u001b[1;31mNotFoundError\u001b[0m: (Status 404) (Quandl Error QECx02) You have submitted an incorrect Dataset code. Please check your Dataset codes and try again."
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "import quandl\n",
    "import my_secrets\n",
    "quandl.ApiConfig.api_key = my_secrets.QUANDL_API_KEY\n",
    "\n",
    "stocks = quandl.get(\"EOD/DIS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividend</th>\n",
       "      <th>Split</th>\n",
       "      <th>Adj_Open</th>\n",
       "      <th>Adj_High</th>\n",
       "      <th>Adj_Low</th>\n",
       "      <th>Adj_Close</th>\n",
       "      <th>Adj_Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-02</th>\n",
       "      <td>37.25</td>\n",
       "      <td>38.50</td>\n",
       "      <td>37.25</td>\n",
       "      <td>37.25</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.142559</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>408858.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-03</th>\n",
       "      <td>37.25</td>\n",
       "      <td>37.88</td>\n",
       "      <td>37.25</td>\n",
       "      <td>37.75</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.140264</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>389370.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-04</th>\n",
       "      <td>37.75</td>\n",
       "      <td>37.88</td>\n",
       "      <td>37.50</td>\n",
       "      <td>37.75</td>\n",
       "      <td>2397.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>0.140264</td>\n",
       "      <td>0.138857</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>467127.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-05</th>\n",
       "      <td>37.75</td>\n",
       "      <td>38.00</td>\n",
       "      <td>37.63</td>\n",
       "      <td>37.88</td>\n",
       "      <td>2397.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>0.140708</td>\n",
       "      <td>0.139338</td>\n",
       "      <td>0.140264</td>\n",
       "      <td>467127.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-08</th>\n",
       "      <td>37.88</td>\n",
       "      <td>38.38</td>\n",
       "      <td>37.00</td>\n",
       "      <td>37.75</td>\n",
       "      <td>3197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.140264</td>\n",
       "      <td>0.142115</td>\n",
       "      <td>0.137005</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>623031.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume  Dividend  Split  Adj_Open  \\\n",
       "Date                                                                        \n",
       "1962-01-02  37.25  38.50  37.25  37.25  2098.0       0.0    1.0  0.137931   \n",
       "1962-01-03  37.25  37.88  37.25  37.75  1998.0       0.0    1.0  0.137931   \n",
       "1962-01-04  37.75  37.88  37.50  37.75  2397.0       0.0    1.0  0.139782   \n",
       "1962-01-05  37.75  38.00  37.63  37.88  2397.0       0.0    1.0  0.139782   \n",
       "1962-01-08  37.88  38.38  37.00  37.75  3197.0       0.0    1.0  0.140264   \n",
       "\n",
       "            Adj_High   Adj_Low  Adj_Close  Adj_Volume  \n",
       "Date                                                   \n",
       "1962-01-02  0.142559  0.137931   0.137931   408858.24  \n",
       "1962-01-03  0.140264  0.137931   0.139782   389370.24  \n",
       "1962-01-04  0.140264  0.138857   0.139782   467127.36  \n",
       "1962-01-05  0.140708  0.139338   0.140264   467127.36  \n",
       "1962-01-08  0.142115  0.137005   0.139782   623031.36  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_periods = 200\n",
    "stocks['SMA'] = stocks['Close'].rolling(n_periods).mean().shift(-int(n_periods/2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks['Above_SMA'] = (stocks['Close'] > stocks['SMA']).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compare this position to the previous, if different, this value is non-zero\n",
    "stocks['PositionChange'] = stocks['Above_SMA'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>SMA</th>\n",
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       "      <th>1962-01-02</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-03</th>\n",
       "      <td>37.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-04</th>\n",
       "      <td>37.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  SMA  Above_SMA  PositionChange\n",
       "Date                                             \n",
       "1962-01-02  37.25  NaN          0             NaN\n",
       "1962-01-03  37.75  NaN          0             0.0\n",
       "1962-01-04  37.75  NaN          0             0.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As you can see, PositionChange is -1 when we crossover from above, and 1 when we crossover from below\n",
    "columns = ['Close', 'SMA', \"Above_SMA\", \"PositionChange\"]\n",
    "stocks[columns].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1962-09-10', '1962-09-14', '1962-11-26', '1962-11-29',\n",
       "               '1963-01-07', '1963-01-09', '1963-02-06', '1963-02-14',\n",
       "               '1963-03-19', '1963-03-21',\n",
       "               ...\n",
       "               '2017-08-09', '2017-11-14', '2018-02-08', '2018-02-28',\n",
       "               '2018-03-07', '2018-03-13', '2018-06-18', '2018-09-11',\n",
       "               '2018-09-14', '2018-10-05'],\n",
       "              dtype='datetime64[ns]', name='Date', length=468, freq=None)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks[stocks['PositionChange'] == -1].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(734869.0, 735963.0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = stocks[['Close', 'SMA']].plot(figsize=(20, 20))\n",
    "ax.vlines(stocks[stocks['PositionChange'] == 1].index, 0, 250,\n",
    "           linestyles='dashed', colors='green', alpha=0.5, linewidths=2)\n",
    "ax.vlines(stocks[stocks['PositionChange'] == -1].index, 0, 250,\n",
    "           linestyles='dashed', colors='red', alpha=0.5, linewidths=2)\n",
    "\n",
    "ax.set_xlim(pd.Timestamp('2013-01-01'), pd.Timestamp('2015-12-31'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Usages\n",
    "\n",
    "* **Long term versus Short term**:\n",
    "    Another usage for the moving average is whether a short term MA (such as 15 days) is above or below the long term (say, 200 day) MA. This is similar to the exercise comparing the price itself to the SMA. If the short term MA crosses from below, it is considered an indicator of a bull market by some.\n",
    "\n",
    "* **Above / Below Curve**: \n",
    "    Another simple indicator people use the SMA or EMA for is whether the current price is above or below the current moving average, rather than finding the point at which it crosses over. We end this module of an example of visualising this data, showing the periods where "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "from vega_datasets import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "import quandl\n",
    "import my_secrets\n",
    "quandl.ApiConfig.api_key = my_secrets.QUANDL_API_KEY\n",
    "\n",
    "stocks = quandl.get(\"EOD/DIS\")\n",
    "n_periods = 200\n",
    "stocks['SMA'] = stocks['Close'].rolling(n_periods).mean().shift(-int(n_periods/2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividend</th>\n",
       "      <th>Split</th>\n",
       "      <th>Adj_Open</th>\n",
       "      <th>Adj_High</th>\n",
       "      <th>Adj_Low</th>\n",
       "      <th>Adj_Close</th>\n",
       "      <th>Adj_Volume</th>\n",
       "      <th>SMA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-02</th>\n",
       "      <td>37.25</td>\n",
       "      <td>38.50</td>\n",
       "      <td>37.25</td>\n",
       "      <td>37.25</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.142559</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>408858.24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-03</th>\n",
       "      <td>37.25</td>\n",
       "      <td>37.88</td>\n",
       "      <td>37.25</td>\n",
       "      <td>37.75</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.140264</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>389370.24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-04</th>\n",
       "      <td>37.75</td>\n",
       "      <td>37.88</td>\n",
       "      <td>37.50</td>\n",
       "      <td>37.75</td>\n",
       "      <td>2397.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>0.140264</td>\n",
       "      <td>0.138857</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>467127.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-05</th>\n",
       "      <td>37.75</td>\n",
       "      <td>38.00</td>\n",
       "      <td>37.63</td>\n",
       "      <td>37.88</td>\n",
       "      <td>2397.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>0.140708</td>\n",
       "      <td>0.139338</td>\n",
       "      <td>0.140264</td>\n",
       "      <td>467127.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-08</th>\n",
       "      <td>37.88</td>\n",
       "      <td>38.38</td>\n",
       "      <td>37.00</td>\n",
       "      <td>37.75</td>\n",
       "      <td>3197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.140264</td>\n",
       "      <td>0.142115</td>\n",
       "      <td>0.137005</td>\n",
       "      <td>0.139782</td>\n",
       "      <td>623031.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume  Dividend  Split  Adj_Open  \\\n",
       "Date                                                                        \n",
       "1962-01-02  37.25  38.50  37.25  37.25  2098.0       0.0    1.0  0.137931   \n",
       "1962-01-03  37.25  37.88  37.25  37.75  1998.0       0.0    1.0  0.137931   \n",
       "1962-01-04  37.75  37.88  37.50  37.75  2397.0       0.0    1.0  0.139782   \n",
       "1962-01-05  37.75  38.00  37.63  37.88  2397.0       0.0    1.0  0.139782   \n",
       "1962-01-08  37.88  38.38  37.00  37.75  3197.0       0.0    1.0  0.140264   \n",
       "\n",
       "            Adj_High   Adj_Low  Adj_Close  Adj_Volume  SMA  \n",
       "Date                                                        \n",
       "1962-01-02  0.142559  0.137931   0.137931   408858.24  NaN  \n",
       "1962-01-03  0.140264  0.137931   0.139782   389370.24  NaN  \n",
       "1962-01-04  0.140264  0.138857   0.139782   467127.36  NaN  \n",
       "1962-01-05  0.140708  0.139338   0.140264   467127.36  NaN  \n",
       "1962-01-08  0.142115  0.137005   0.139782   623031.36  NaN  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks['Date'] = stocks.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks['Gap'] = stocks['Close'] - stocks[\"SMA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"layer\": [{\"data\": {\"url\": \"altair-data-ffe60e6ef07d7af27a6be7e6046cdfee.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"condition\": {\"value\": \"#7fc97f\", \"test\": \"(datum.Close > datum.SMA)\"}, \"value\": \"#fdc086\"}, \"x\": {\"type\": \"temporal\", \"field\": \"Date\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"Close\"}}, \"width\": 600}, {\"data\": {\"url\": \"altair-data-ffe60e6ef07d7af27a6be7e6046cdfee.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"line\", \"encoding\": {\"color\": {\"value\": \"red\"}, \"x\": {\"type\": \"temporal\", \"field\": \"Date\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"SMA\"}}, \"width\": 600}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\"};\nvar opt = {};\nvar type = \"vega-lite\";\nvar id = \"49f1266a-d62f-4c75-8c8e-03507c6ea01b\";\n\nvar output_area = this;\n\nrequire([\"nbextensions/jupyter-vega/index\"], function(vega) {\n  var target = document.createElement(\"div\");\n  target.id = id;\n  target.className = \"vega-embed\";\n\n  var style = document.createElement(\"style\");\n  style.textContent = [\n    \".vega-embed .error p {\",\n    \"  color: firebrick;\",\n    \"  font-size: 14px;\",\n    \"}\",\n  ].join(\"\\\\n\");\n\n  // element is a jQuery wrapped DOM element inside the output area\n  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n  // IPython.display.html#IPython.display.Javascript.__init__\n  element[0].appendChild(target);\n  element[0].appendChild(style);\n\n  vega.render(\"#\" + id, spec, type, opt, output_area);\n}, function (err) {\n  if (err.requireType !== \"scripterror\") {\n    throw(err);\n  }\n});\n",
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x7faf645756a0>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#49f1266a-d62f-4c75-8c8e-03507c6ea01b"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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"
     },
     "metadata": {
      "jupyter-vega": "#49f1266a-d62f-4c75-8c8e-03507c6ea01b"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#49f1266a-d62f-4c75-8c8e-03507c6ea01b"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#49f1266a-d62f-4c75-8c8e-03507c6ea01b"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart1 = alt.Chart(stocks[\"2010\":]).mark_bar().encode(\n",
    "    x=\"Date:T\",\n",
    "    y=\"Close:Q\",\n",
    "    color=alt.condition(\n",
    "        alt.datum.Close > alt.datum.SMA,\n",
    "        alt.value(\"#7fc97f\"),  # The positive color\n",
    "        alt.value(\"#fdc086\")  # The negative color\n",
    "    )\n",
    ").properties(width=600)\n",
    "\n",
    "chart2 = alt.Chart(stocks[\"2010\":]).mark_line().encode(\n",
    "    x=\"Date:T\",\n",
    "    y=\"SMA:Q\",\n",
    "    color=alt.value(\"red\")\n",
    ").properties(width=600)\n",
    "\n",
    "chart1 + chart2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
